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10 Things to Consider While Building your Right Data Science Team

Enterprises these days no longer follow outdated business processes. The focus is more on adopting the latest technology and relying on new software and tools to increase productivity and ROI. But working with advanced systems means hiring experts who have experience in the said field.

Before we talk in detail about why and how you should build a successful data science team, let us first see what it actually is. The simplest definition is-

In computer science, data analytics is the process of analyzing raw data to make conclusions about it.

Not every business has the suitable infrastructure to build a data science team. While some find it easy, others have to make a lot of effort. A global digital framework can introduce governance, the social environment, business, and technology. In such instances, hiring the services of a data science company is a better option.

A data architect has an evolving role, so there is no industry-standard certification program. As data engineers, data scientist experts, or solutions architects, individuals typically gain experience in data design, data management, and data storage work as they work their way up to the role of a data architect.

What if you want to build a team for data science projects? At what stage should you introduce data science into your enterprise? Mostly, the decision has the highest impact at early stages only.

How important is it to build the right team?

Introducing data science roles into your business processes requires a lot of planning. You will need to be sure that you have enough budget to invest in systems, people, and processes. You also need to be assured that your existing employees will welcome the changes and embrace them. If your employees do not value the insights offered by data scientists, the purpose will be lost.
Many companies expect data analysts to be able to convert alienating numbers in order to provide tangible insights.

A data science team has multiple experts, each dealing with different aspects of the field. The roles and responsibilities of the team members depend on their experience in domain expertise, technical knowledge, and quantitative skills.

The actual team positions might differ, depending on the types of data science teams an enterprise wants to build and how much it can invest into it.

The data science team structure can further be classified. You will first need to decide the type of team you want to build in your organization and then hire the right kind of experts who require data analysts with market-tested skills.

So does this make you wonder who should the data science team report to? Well, the answer lies in the structure of the team chosen by the enterprise. In large enterprises, the team reports to the COO, CTO, CPO, or CFO (further reporting by multiple teams). In the centralized model, the team reports to the head of data analytics.

It is not casual to build data science teams. You will need to consider a lot of factors in choosing every member of the team and assigning them their respective team roles.

The methods and viewpoints we use are different based on the skill sets and expertise of the individuals in our team. Moreover, data science is a team sport, so accurate teamwork is essential!

How to build a data science team from scratch?

Take a look at the factors mentioned below.

One of the main reasons for having a data science team in your organization is to load balance the machine learning models across the business. The responsibility of managing the team and ensuring that they are delivering the required data insights lies with the team leader or the Chief Analytics Officer/Chief Data Officer.

Eventually, they make their way to being business leaders.

Though most data scientists and analysts are exceptionally talented at their work, we need to emphasize their leadership skills, too, when choosing the team data science head. Someone who has strong domain expertise, problem-solving abilities, reasoning, and data-driven decision-making skills and someone who can communicate with employees across the levels will be suitable for the role.

Apart from the command over how data science functions effectively, the individuals should preferably be well versed with business analysis too. Harvard Business Review provides exceptional content on new ideas and how leadership should be at the time when business problems emerge.

The data scientists, machine learning engineers, and other analysts (software engineers in rare scenarios) should have worked on at least a handful of big data and data science field projects. They should have successfully built models to gather and collect data, process it, and derive accurate insights.

Data preparation in a most interactive way implies converting business expectations into useful insights with the data team.

For a data team to be well-rounded, you will need to hire people with varied academic qualifications related to the field. For example, a machine learning engineer comes from an engineering background. An ML Engineer’s job might include specific tasks on recommendation engines.

A business analyst would come from a statistics and mathematics background.

When building data science capability for your enterprise, start by looking within the enterprise. Before you hire external talent, make sure you have searched for options from within the business. Some of your employees might have been doing certification courses or working on ML models as a personal project. They are much familiar with model training and have a deeper understanding.

You can also list out the potential employees who can be trained to work with external experts and become a part of the data science projects and the majority of complex data science tasks. This will make it easy to adopt the new processes in the enterprise. And most importantly, it will give you an exact idea of the talent gap you are facing.

Though the task of the data science team is to gather insights from a large amount of data, it doesn’t mean they do not have to be aware of the business. Some data engineering teams indeed work on data without knowing what they are arriving at or how it will help the business (if it will even help).

Additionally, there are various aspects on an operational level that could address a business problem in day-to-day interpretation activities. Project managers along with the whole team have different responsibilities and work closely so the approach entails the best way for data-related use cases.

Domain knowledge is a must for any job. The one person you hire should have expertise in the field they work. They should have command over the topics, tools, techniques, processes, and systems that belong to their domain.

As an example, a technical aspect can be “Central tendency” — a single value that identifies the central point within a set of data in an attempt to describe that set of data as a whole.

Database management, programming, computing, continuous integration of tools and systems, working on the cloud platforms, etc., are possible only when your data science manager or the entire team has the required technical skills as well.

For example, the group of data science experts must have hands-on experience with data-related functions used in Python which might play an important role in complex projects.

Another thing could be implementing “The Team Data Science Process (TDSP)”. It is an adaptive data science approach for creating predictive analytics applications and intelligent programs.

A lot of teams do not achieve their targets because of miscommunication and lack of proper collaboration between the team members. This led to a few multinational companies coming up with team-building exercises as a part of successful data science teams’ best practices.

From organizing outings to facilitating multi-level communication within the organization, businesses are focusing on building teams where the team members can express their views, be heard, and learn from each other.

How can you build the right data science if the professionals don’t have the passion to build new models, discover insights, and provide valuable reports for the enterprise? You also need to make sure that the team is always motivated to deliver its best.

From working on new algorithms to solving data-focused personalization use cases. An engineer builds data pipelines and comes up with relevant questions that further add to a custom-built solution. Like if someone has prior experience to build recommendation systems, then that person is contributing to a big picture whether in a single category or more than two types.

Right people often fit into many roles in managing a data house.

Data science and data analytics firms think of the future when they build teams of experts to assist various SMEs and organizations from around the world. That’s how they provide the best data analytics services in the market. Hiring the services of data analytics firms can give your business the required power to forge ahead of your competitors.

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